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Parallel

Subdivide A Problem To A Pool Of Workers (No Shared Data)

ruby

array, threads, answers = ["ab", "we", "tfe", "aoj"], [], []
array.each { |word|
threads << Thread.new(word.split '' ) do |x|
answer = []
x.each { |a|
answer << a
x.each { |b| answer << [a, b].join }
}
answers << answer
end
}
threads.each {|thr| thr.join}
answers

cpp

vector<string> input;
input.push_back("ab");
input.push_back("we");
input.push_back("tfe");
input.push_back("aoj");

// Make the capacity for 'output' the same as 'input'
vector<set<string> > output(input.size());
#pragma omp parallel for
for (int i = 0; i < input.size(); ++i) {
set<string> perms;
generate_perms(input[i], perms);
#pragma omp critical
// Must use operator[]() and not push_back() since this line
// might be called in any order with respect to 'i'
output[i] = perms;
}

cout << output << endl;

clojure

(defn perm-chars [l]
"Returns a list of all possible permutations of strings with the
same size as the input string. This function will return duplicates
if the same character occurs multiple time in the string.
Ex: ab -> (aa ab ba ab)"
(if (string? l)
(recur (repeat (count l) l))
(let [s (first l)
r (rest l)]
(if (empty? r)
(map identity s)
(->> s
(map (fn [c] (map #(str c %) (perm-chars r))))
(flatten))))))

(defn perm-sz [s]
"Returns a list of all possible permutations of the input
string. May return duplicats.
Ex: ab -> (aa ab ba bb a b a b)"
(if-not (empty? s)
(let [r (perm-chars s)]
(if (= (count s) 1)
r
(->> r
(map #(perm-sz (apply str (rest %))))
(flatten)
(lazy-cat r))))))

(defn perm [s]
"Returns a list of all possible permutations of the input
string. The list of string is sorted and does not contain
duplicates.
Ex: ab -> (a aa ab b ba bb)"
(->> (reduce (fn [s e] (conj s e)) #{} (perm-sz s))
(map str)
(sort)))

(println (pmap perm ["ab" "we" "tfe" "aoj"]))

(require 'cojure.contrib.combinatorics)

(pmap (fn [str]
(apply concat (map #(selections str (inc %))
(range (count str)))))
["ab", "we", "tfe", "aoj"])

Subdivide A Problem To A Pool Of Workers (Shared Data)

Take a hard to compute problem and split it up between multiple worker threads. In your solution, try to fully utilize available cores or processors. (I'm looking at you, Python!)

Note: In this question, there should be a need for shared state between worker threads while the problem is being solved.

Example:

-Conway Game of Life-

From Wikipedia:

The universe of the Game of Life is an infinite two-dimensional orthogonal grid of square cells, each of which is in one of two possible states, live or dead. Every cell interacts with its eight neighbors, which are the cells that are directly horizontally, vertically, or diagonally adjacent. At each step in time, the following transitions occur:

1. Any live cell with fewer than two live neighbours dies, as if caused by underpopulation.
2. Any live cell with more than three live neighbours dies, as if by overcrowding.
3. Any live cell with two or three live neighbours lives on to the next generation.
4. Any dead cell with exactly three live neighbours becomes a live cell.

The initial pattern constitutes the seed of the system. The first generation is created by applying the above rules simultaneously to every cell in the seed—births and deaths happen simultaneously, and the discrete moment at which this happens is sometimes called a tick (in other words, each generation is a pure function of the one before). The rules continue to be applied repeatedly to create further generations.

--However, for our purposes, we will assign a size to the game "board": 2^k * 2^k . That is, the board should be easy to subdivide.

Notice that in this problem, at each step or "tick", each thread/process will need to share data with its neighborhood.

clojure

; This is a "glider"
(def *start*
[".O......"
"..O....."
"OOO....."
"........"
"........"
"........"
"........"])
(def *width* (count (first *start*)))
(def *height* (count *start*))
(def *live* \O)
(def *dead* \.)
(def *n-generations-to-show* 3)

(defn cell-at
([b coord]
(cell-at b coord {:col 0 :row 0}))
([b coord offset]
(let [x (mod (+ (:col coord) (:col offset)) *width*)
y (mod (+ (:row coord) (:row offset)) *height*)]
(nth (nth b y) x))))

(defn neighbor-count [b coord]
(->> (for [x (range -1 2) y (range -1 2)] {:col x :row y})
(filter #(not (= {:col 0 :row 0} %)))
(map (partial cell-at b coord))
(reduce (fn [sum n] (+ sum (if (= *live* n) 1 0))) 0)))

(defn next-generation-cell [b coord]
(let [nc (neighbor-count b coord)]
(cond (< nc 2) *dead*
(> nc 3) *dead*
(= nc 3) *live*
true (cell-at b coord))))

(defn next-generation-row [b row]
(->> (range *width*)
(map #(next-generation-cell b {:col % :row row}))
(apply str)))

(defn next-generation [b]
(->> (range *height*)
(pmap #(next-generation-row b %))))

(defn generation-seq [b]
(let [ng (next-generation b)]
(lazy-seq (cons ng (generation-seq ng)))))

(doseq [g (take *n-generations-to-show* (generation-seq *start*))]
(doseq [l g]
(println l))
(println))

(shutdown-agents)

; This version calculates each separate line on a separate thread (pmap in next-generation)

Threads

Create a multithreaded "Hello World"

Create a program which outputs the string "Hello World" to the console, multiple times, using separate threads or processes.

Example:

-Output-

Thread one says Hello World!
Thread two says Hello World!
Thread four says Hello World!
Thread three says Hello World!

-Notice that the threads can print in any order.

ruby

%w[one two three four].each do |number|
Thread.new(number) { |number|
puts "Thread #{number} says Hello World!"
}.join
end

cpp

#include <iostream>
#include <string>

using namespace std;

int main(){
int pid;
string text[4]={"one","two","three","four"};
for (int i=0;i<4;i++){
pid=fork();
if (pid>0){
//cout << "Process("<<pid<<") - " << "Thread " << text[i] << " says Hello World!" << endl;
cout << "Thread " << text[i] << " says Hello World!" << endl;
exit(0);
}
}
return 0;
}

#include <iostream>
#include <string>

#include <omp.h>

int main() {
unsigned int const num_threads = 4;

std::string const names[] = { "one", "two", "three", "four" };

# pragma omp parallel num_threads(num_threads)
{
unsigned const id = omp_get_thread_num();
// Stream concatenation isn't thread-safe so we use a critical section.
# pragma omp critical
std::cout << "Thread " << names[id] << " says Hello World!" << std::endl;
}
}

clojure

(doseq [msg ["one" "two" "three" "four"]]
(future (println "Thread" msg "says Hello World!")))

(dorun (pmap #(println (str "Thread " % " says Hello World!")) '("one" "two" "three" "four")))

(dorun (map (fn [n] (.start (Thread. #(println (str "Thread " n " says Hello World!")))))
'("one" "two" "three" "four")))

Create read/write lock on a shared resource.

Create multiple threads or processes who are either readers or writers. There should be more readers then writers.

(From Wikipedia):

Multiple readers can read the data in parallel but an exclusive lock is needed while writing the data. When a writer is writing the data, readers will be blocked until the writer is finished writing.

Example:

-Output-

Thread one says that the value is 8.
Thread three says that the value is 8.
Thread two is taking the lock.
Thread four tried to read the value, but could not.
Thread five tried to write to the value, but could not.
Thread two is changing the value to 9.
Thread two is releasing the lock.
Thread four says that the value is 9.
...

--Notice that when a needed resource is locked, a thread can set a timer and try again in the future, or wait to be notified that the resource is no longer locked.

cpp

class reader
{
string name_;
public:
reader(const string& name) : name_(name) {}

void operator()() {
for (;;this_thread::sleep(posix_time::milliseconds(1)))
{
shared_lock<shared_mutex> lock(m, try_to_lock);
lock_guard<mutex> cout_lock(io_m);
cout << "Thread " << name_;
if (lock)
cout << " says that the value is " << shared_value << "." << endl;
else
cout << " tried to read the value, but could not." << endl;
}
}
};

class writer
{
string name_;
public:
writer(const string& name) : name_(name) {}
void operator()() {
for (;;this_thread::sleep(posix_time::milliseconds(1)))
{
unique_lock<shared_mutex> lock(m, try_to_lock);
lock_guard<mutex> cout_lock(io_m);
cout << "Thread " << name_;
if (lock)
{
cout << " is taking the lock." << endl;
shared_value = rand() % 10;
cout << "Thread " << name_ << " is changing the value to " << shared_value << endl;
cout << "Thread " << name_ << " is releasing the lock. " << endl;
}
else
cout << " tried to write to the value, but could not." << endl;
}
}
};

int main()
{
thread t1 = thread(reader("one"));
thread t2 = thread(reader("two"));
thread t3 = thread(reader("three"));
thread t4 = thread(writer("four"));
writer("five")();
}

clojure

; NOTE! Using explicit locking is NOT the Clojure way. It was done
; this way in order to comply exactly with the problem
; specification. Sharing data in Clojure would normally be done by
; using "atom", "agent" or "ref" depending on situation. None of those
; methods would ever result in the reader not being able to read (as
; required by the problem) since reading is wait-free in clojure.

(def *readers* (map #(agent %) '("one" "two" "three")))
(def *writers* (map #(agent %) '("four" "five")))
(def *mutex* (agent :unlocked))
(def *value* 0)

; mutex implementation
(defn lock [state who success-fn fail-fn]
(send who (if (= state :locked) fail-fn success-fn))
:locked)

(defn unlock [mutex]
:unlocked)

; Must be invoked with send-off since this handler blocks
(defn rand-sleep [state next-fn]
(Thread/sleep (rand-int 5))
(send *agent* next-fn)
state)

; Reader functions
(declare try-read)

(defn reader-got-lock [name]
(println (format "Thread %s says that the value is %d." name *value*))
(send *mutex* unlock)
(send-off *agent* rand-sleep try-read)
name)

(defn reader-did-not-get-lock [name]
(println (format "Thread %s tried to read the value, but could not." name))
(send-off *agent* rand-sleep try-read)
name)

(defn try-read [name]
(send *mutex* lock *agent* reader-got-lock reader-did-not-get-lock)
name)

; Writer functions
(declare try-write)

(defn writer-got-lock [name]
(println (format "Thread %s is taking the lock." name))
(def *value* (rand-int 10))
(println (format "Thread %s is changing the value to %d." name *value*))
(send *mutex* unlock)
(println (format "Thread %s is relasing the lock." name))
(send-off *agent* rand-sleep try-write)
name)

(defn writer-did-not-get-lock [name]
(println (format "Thread %s tried to write the value, but could not." name))
(send-off *agent* rand-sleep try-write)
name)

(defn try-write [name]
(send *mutex* lock *agent* writer-got-lock writer-did-not-get-lock)
name)

(dorun (map #(send % try-write) *writers*))
(dorun (map #(send % try-read) *readers*))

Separate user interaction and computation.

Allow your program to accept user interaction while conducting a long running computation.

Example:

Hello user! Please input a string to permute: (input thread)
abcdef
Passing on abcdef... (input thread)
Please input another string to permute: (input thread)
lol
Passing on lol... (input thread)
Done Work On abcdef! (worker thread)
["abcdef", "abcefd", ... ] (worker thread)
Please input another string to permute: (input thread)
EXIT
Quitting, I

'll let my worker thread know... (input thread)

We'

re quitting! Alright! (worker thread)

--Notice, that this could be accomplished on the command line or within a GUI. The point is that computation and user interaction should take place on separate threads of control.

cpp

class bg_worker
{
mutex bg_mutex_;
condition_variable work_present_;
deque<string> work_queue_;

result calc_perm(string s) {
result perms = result(new list<string>());

// sleep to simulate lots of work...
this_thread::sleep(posix_time::seconds(3));
sort(s.begin(), s.end());
do {
perms->push_back(s);
} while (next_permutation(s.begin(), s.end()));
return perms;
}

public:
void submit_work(const string &s) {
lock_guard<mutex> lock(bg_mutex_);
work_queue_.push_back(s);
work_present_.notify_one();
}

void operator()() {
for (;;) {
unique_lock<mutex> lock(bg_mutex_);
while (work_queue_.empty())
work_present_.wait(lock);
string s = work_queue_.front();
work_queue_.pop_front();
lock.unlock();

if (s == "EXIT") {
lock_guard<mutex> cout_lock(cout_mutex);
cout << "We're quitting! Alright!" << endl;
break;
}

result perm = calc_perm(s);
lock_guard<mutex> cout_lock(cout_mutex);
cout << "Done Work On " << s << "!" << endl;
cout << perm << endl;
}
}
};

int main()
{
bg_worker worker;
thread bg_thr(boost::ref(worker));
bool done = false;

{
lock_guard<mutex> cout_lock(cout_mutex);
cout << "Hello user! Please input a string to permute:" << endl;
}

while (!done)
{
string input;
cin >> input;
{
lock_guard<mutex> cout_lock(cout_mutex);
if (input == "EXIT") {
cout << "Quitting, I'll let my worker thread know..." << endl;
done = true;
} else {
cout << "Passing on " << input << "..." << endl;
cout << "Please input another string to permute:" << endl;
}
}
worker.submit_work(input);
}

bg_thr.join();
}

clojure

(defn background-computation [_ s]
(let [res (permutations s)]
(println (format "Done Work On %s!" s))
(println res)))

(defn shutdown-app [_]
(println "We're quitting! Alright!")
(shutdown-agents))

(println "Hello user! Please input a string to permute: ")
(let [worker-agent (agent nil)]
(loop [input (str (read))]
(if (= input "EXIT")
(do (println "Quitting, I'll let my worker thread know...")
(send worker-agent shutdown-app))
(do (println (format "Passing on %s..." input))
(send worker-agent background-computation input)
(println "Please input another string to permute: ")
(recur (str (read)))))))